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FiPy is a finite volume partial differential equation (PDE) solver written in Python and designed for building, testing, and running PDE-based models with a clean, scriptable workflow. If you’re installing it for the first time, your main goal should be a setup that is stable, reproducible, and easy to upgrade. In practice, that usually means installing FiPy into its own environment, verifying that core dependencies work, and then optionally enabling faster solver backends (like PETSc) when you need performance or scalability.

This guide walks you through a reliable installation path, a quick validation checklist, and a troubleshooting section for the most common issues people hit during their first setup. The steps below follow the official FiPy installation guidance and common best practices for scientific Python environments. FiPy also provides a ready-to-use environment on Binder for quick exploration, but for serious work you’ll want a local environment you control.

What you need before you start

  • A modern Python environment manager (conda is recommended for first-time installs)
  • Basic command line access (Terminal on macOS/Linux, PowerShell/Command Prompt on Windows)
  • Enough disk space for scientific packages (a few GB is normal)
  • A plan for solvers: start with the default, then add PETSc later if needed

Recommended installation method: conda (most reliable)

FiPy’s documentation explicitly describes conda-based installation as the recommended path, because it resolves compiled scientific dependencies more consistently across platforms and reduces “it installed but doesn’t run” situations. The conda-forge channel provides an actively maintained FiPy package and frequently updates builds. You can install FiPy directly from conda-forge as a single command, but it’s best practice to create a dedicated environment first.

Step 1: Install conda (or a conda-compatible tool)

If you already have conda installed (Anaconda, Miniconda, or another conda-compatible distribution), you can skip this step. If not, install a lightweight conda distribution and make sure the conda command works in your terminal.

Step 2: Create a dedicated environment

Creating a clean environment keeps dependencies isolated. That makes upgrades easier and prevents conflicts with unrelated projects. 

conda create -n fipy-env python=3.11
conda activate fipy-env

You can pick a different Python version if your project requires it, but sticking to a modern, supported version is usually simplest.

Step 3: Install FiPy from conda-forge

The conda-forge package page lists the installation command. In a fresh environment, this is typically the smoothest approach. 

conda install -c conda-forge fipy

If you use mamba (a faster conda-compatible solver), you can install the same package with: 

mamba install -c conda-forge fipy

If you want strict channel priority (often reduces conflicts), configure conda-forge as a priority channel: 

conda config --add channels conda-forge
conda config --set channel_priority strict

Alternative installation: pip (works, but be careful)

FiPy is also available on PyPI, so you can use pip. The most common pitfall with pip installs for scientific packages is attempting to install into an “empty” environment without core numerical dependencies, which can cause build-time failures or missing prerequisites. If you use pip, do it in a virtual environment and install NumPy first (or make sure it’s present), then install FiPy.

Step 1: Create and activate a virtual environment

python -m venv fipy-venv

Activate it:

# macOS / Linux
source fipy-venv/bin/activate

# Windows (PowerShell)
.\fipy-venv\Scripts\Activate.ps1

Step 2: Upgrade pip tools, then install core numerical deps

python -m pip install --upgrade pip setuptools wheel
python -m pip install numpy

Step 3: Install FiPy

python -m pip install fipy

If pip installation fails on your platform, the quickest pragmatic fix is usually to switch to the conda method above, because conda-forge handles compiled dependencies more smoothly.

Verify your installation

After installing, confirm that FiPy imports and that you can run a small diffusion problem. Start with two checks: version visibility and a minimal simulation.

Check 1: Import FiPy

python -c "import fipy; print('FiPy imported:', fipy.__version__)"

Check 2: Run a tiny diffusion example

Create a file named quick_fipy_test.py: 

from fipy import CellVariable, Grid1D, DiffusionTerm, Viewer

mesh = Grid1D(nx=50, dx=1.0)
phi = CellVariable(name="phi", mesh=mesh, value=0.0)

# Set a Dirichlet boundary condition by directly constraining boundary faces
phi.constrain(1.0, mesh.facesLeft)
phi.constrain(0.0, mesh.facesRight)

eq = DiffusionTerm()
for _ in range(20):
    eq.solve(var=phi, dt=1.0)

print("Min/Max:", float(phi.min()), float(phi.max()))

# Viewer may open a window depending on your environment
# Comment this out if you're on a headless machine
try:
    viewer = Viewer(vars=phi)
    viewer.plot()
except Exception as e:
    print("Viewer skipped:", e)

Run it:

python quick_fipy_test.py

Expected outcome: you should see a min/max range between 0 and 1, and optionally a simple plot if your environment supports a GUI viewer. If you’re on a server or a headless setup, it’s normal for the viewer to fail; the numeric output is the important part.

Solvers and performance: when to add PETSc

FiPy can run with different solver backends. For early learning and small problems, the default setup is usually enough. When you start solving larger systems, running 2D/3D problems with fine meshes, or using stiff multiphysics couplings, solver choice matters. PETSc is a common step up because it provides robust linear and nonlinear solvers, scalable preconditioners, and parallel capabilities.

FiPy’s documentation includes PETSc-related configuration options and solver classes. A practical workflow is:

  1. Get FiPy working locally with the default solver.
  2. Validate a small script and confirm your boundary conditions and discretization are correct.
  3. Only then add PETSc and switch solver suites when performance becomes a bottleneck.

Enabling PETSc (high-level guidance)

PETSc installation varies by platform. On Linux and macOS, conda-forge often provides a smooth path; on native Windows, PETSc availability can be more limited and many users rely on WSL2 for PETSc-based workflows. If you install PETSc and then want to see what solver options are available, FiPy’s usage docs mention running with “-help” style options for PETSc-enabled solvers to list configuration possibilities.

Common first-time setup mistakes (and how to avoid them)

Mistake 1: Installing into your base environment

Installing scientific stacks into the base environment often leads to dependency conflicts later. Use a dedicated environment so you can safely upgrade or remove packages without breaking other projects.

Mistake 2: Mixing conda and pip without a plan

You can mix conda and pip, but do it intentionally. A typical safe approach is: install core numerical packages with conda, then use pip only for packages unavailable on conda-forge. If you use pip first, you can end up pulling incompatible wheels and creating solver conflicts.

Mistake 3: Viewer errors are treated as “FiPy is broken”

Viewer/plot windows depend on your OS, display server, and GUI backend. In headless environments, a viewer error is expected. Validate the numeric output first. If you need plots on servers, use notebook-based plotting or save arrays to files for visualization.

Mistake 4: Confusing FiPy with similarly named hardware

There is also a “FiPy” microcontroller board used in IoT contexts. This guide is about the Python finite volume PDE solver from NIST and the scientific Python ecosystem. If you see documentation about expansion boards or cellular modems, you’re looking at a different product.

Troubleshooting checklist

If your install fails or FiPy won’t import, go through this checklist in order:

1) Confirm you’re in the right environment

which python
python -V
python -c "import sys; print(sys.executable)"

On Windows, use: 

where python

2) Confirm FiPy is installed in that environment

python -c "import fipy; print(fipy.__version__)"

3) If using conda, check for conflicts

conda list
conda info

If conda reports conflicts during install, create a fresh environment and try again. This is often faster than fighting a tangled dependency set.

4) If using pip, confirm NumPy is present

python -c "import numpy; print(numpy.__version__)"

5) Validate with a minimal script

Use the diffusion test in the verification section. It isolates core components: mesh, variable, boundary constraints, an equation term, and time-stepping.

Optional: a clean project structure for your first FiPy work

Once FiPy runs, create a small project layout that supports iteration and reproducibility: 

my-fipy-project/
  env/
  scripts/
    quick_fipy_test.py
    my_first_model.py
  data/
  results/
  README.txt

Keep scripts small at first. FiPy models become easier to maintain when you separate:

  • physical parameters and constants
  • mesh definition and discretization choices
  • equations and boundary conditions
  • time-stepping loop and output

Next steps after installation

After your first successful run, the best next step is to reproduce a standard example: diffusion, convection-diffusion, or a simple phase-field model. Build confidence in your discretization and boundary conditions before jumping into complex multiphysics coupling. If your end goal is phase field, plan your progression:

  1. Diffusion with fixed boundary constraints
  2. Time-dependent diffusion with stable time steps
  3. Nonlinear terms and solver stability checks
  4. A minimal Cahn–Hilliard or Allen–Cahn prototype
  5. Performance upgrades (PETSc) once the model is correct

If you’re writing a longer academic document and want to frame your setup steps clearly, you can also reference a dissertation introduction resource here:
dissertation introduction.

Summary

For first-time installation, conda with a dedicated environment is the most reliable route. Verify your setup with a minimal diffusion script, treat viewer issues as optional, and only add PETSc when you actually need higher performance or more robust solvers. With a stable environment and a working “hello PDE” script, you’re ready to move into FiPy’s more advanced workflows with much less friction.